Jason D. Lee

ORCID

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 225

Top Venues

Publications

Uijeong Jang, Jason D. Lee, Ernest K. Ryu
LoRA Training in the NTK Regime has No Spurious Local Minima. CoRR (2024)
Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark. CoRR (2024)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
Provable Reward-Agnostic Preference-Based Reinforcement Learning. ICLR (2024)
Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon Shaolei Du, Jason D. Lee, Wei Hu
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. ICLR (2024)
Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos
Teaching Arithmetic to Small Transformers. ICLR (2024)
Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kianté Brantley, Dipendra Misra, Jason D. Lee, Wen Sun
Dataset Reset Policy Optimization for RLHF. CoRR (2024)
Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. CoRR (2024)
Hong Jun Jeon, Jason D. Lee, Qi Lei, Benjamin Van Roy
An Information-Theoretic Analysis of In-Context Learning. CoRR (2024)
Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity. CoRR (2024)
Angeliki Giannou, Liu Yang, Tianhao Wang, Dimitris Papailiopoulos, Jason D. Lee
How Well Can Transformers Emulate In-context Newton's Method? CoRR (2024)
Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee
Optimal Multi-Distribution Learning. COLT (2024)
Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. CoRR (2024)
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D. Lee, Deming Chen, Tri Dao
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads. CoRR (2024)
Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee
Scaling Laws in Linear Regression: Compute, Parameters, and Data. CoRR (2024)
Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du
Settling the sample complexity of online reinforcement learning. COLT (2024)
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee
Provably Efficient CVaR RL in Low-rank MDPs. ICLR (2024)
Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon Shaolei Du
Horizon-Free Regret for Linear Markov Decision Processes. ICLR (2024)
James Liu, Guangxuan Xiao, Kai Li, Jason D. Lee, Song Han, Tri Dao, Tianle Cai
BitDelta: Your Fine-Tune May Only Be Worth One Bit. CoRR (2024)
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Provable Offline Preference-Based Reinforcement Learning. ICLR (2024)
Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He
REST: Retrieval-Based Speculative Decoding. NAACL-HLT (2024)
Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang
Neural Temporal Difference and Q Learning Provably Converge to Global Optima. Math. Oper. Res. 49 (1) (2024)
Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna
Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). COLT (2024)
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun
REBEL: Reinforcement Learning via Regressing Relative Rewards. CoRR (2024)
Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon S. Du
Horizon-Free Regret for Linear Markov Decision Processes. CoRR (2024)
Jason D. Lee, Kazusato Oko, Taiji Suzuki, Denny Wu
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit. CoRR (2024)
Eshaan Nichani, Alex Damian, Jason D. Lee
How Transformers Learn Causal Structure with Gradient Descent. CoRR (2024)
Zihao Wang, Eshaan Nichani, Jason D. Lee
Learning Hierarchical Polynomials with Three-Layer Neural Networks. ICLR (2024)
Alex Damian, Loucas Pillaud-Vivien, Jason D. Lee, Joan Bruna
Computational-Statistical Gaps in Gaussian Single-Index Models. CoRR (2024)
Jingfeng Wu, Vladimir Braverman, Jason D. Lee
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. NeurIPS (2023)
Zhenyu He, Zexuan Zhong, Tianle Cai, Jason D. Lee, Di He
REST: Retrieval-Based Speculative Decoding. CoRR (2023)
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora
Fine-Tuning Language Models with Just Forward Passes. NeurIPS (2023)
Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee
Provably Efficient CVaR RL in Low-rank MDPs. CoRR (2023)
Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. CoRR (2023)
Eshaan Nichani, Alex Damian, Jason D. Lee
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks. NeurIPS (2023)
Alex Damian, Eshaan Nichani, Jason D. Lee
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability. ICLR (2023)
Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. CoRR (2023)
Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR (2023)
Ziang Song, Tianle Cai, Jason D. Lee, Weijie J. Su
Reward Collapse in Aligning Large Language Models. CoRR (2023)
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris S. Papailiopoulos
Looped Transformers as Programmable Computers. CoRR (2023)
Eshaan Nichani, Alex Damian, Jason D. Lee
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks. CoRR (2023)
Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition. AISTATS (2023)
Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon S. Du, Jason D. Lee
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. CoRR (2023)
Tianle Cai, Kaixuan Huang, Jason D. Lee, Mengdi Wang
Scaling In-Context Demonstrations with Structured Attention. CoRR (2023)
Xinyi Chen, Edgar Minasyan, Jason D. Lee, Elad Hazan
Regret Guarantees for Online Deep Control. L4DC (2023)
Wenhao Zhan, Jason D. Lee, Zhuoran Yang
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. ICLR (2023)
Zhuoqing Song, Jason D. Lee, Zhuoran Yang
Can We Find Nash Equilibria at a Linear Rate in Markov Games? ICLR (2023)
Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. NeurIPS (2023)
Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS (2023)
Etash Kumar Guha, Jason D. Lee
Solving Robust MDPs through No-Regret Dynamics. CoRR (2023)
Sadhika Malladi, Tianyu Gao, Eshaan Nichani, Alex Damian, Jason D. Lee, Danqi Chen, Sanjeev Arora
Fine-Tuning Language Models with Just Forward Passes. CoRR (2023)
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Provable Offline Reinforcement Learning with Human Feedback. CoRR (2023)
Hanlin Zhu, Ruosong Wang, Jason D. Lee
Provably Efficient Reinforcement Learning via Surprise Bound. AISTATS (2023)
Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms. NeurIPS (2023)
Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Shaolei Du, Jason D. Lee
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. ICML (2023)
Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. CoRR (2023)
Zihan Zhang, Wenhao Zhan, Yuxin Chen, Simon S. Du, Jason D. Lee
Optimal Multi-Distribution Learning. CoRR (2023)
Hadi Daneshmand, Jason D. Lee, Chi Jin
Efficient displacement convex optimization with particle gradient descent. CoRR (2023)
Jingfeng Wu, Vladimir Braverman, Jason D. Lee
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability. CoRR (2023)
Kaifeng Lyu, Jikai Jin, Zhiyuan Li, Simon S. Du, Jason D. Lee, Wei Hu
Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. CoRR (2023)
Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, Kangwook Lee, Dimitris Papailiopoulos
Teaching Arithmetic to Small Transformers. CoRR (2023)
Zihao Wang, Eshaan Nichani, Jason D. Lee
Learning Hierarchical Polynomials with Three-Layer Neural Networks. CoRR (2023)
Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. ICML (2023)
Zhuoqing Song, Jason D. Lee, Zhuoran Yang
Can We Find Nash Equilibria at a Linear Rate in Markov Games? CoRR (2023)
Baihe Huang, Banghua Zhu, Hanlin Zhu, Jason D. Lee, Jiantao Jiao, Michael I. Jordan
Towards Optimal Statistical Watermarking. CoRR (2023)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
PAC Reinforcement Learning for Predictive State Representations. ICLR (2023)
Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du
Settling the Sample Complexity of Online Reinforcement Learning. CoRR (2023)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
How to Query Human Feedback Efficiently in RL? CoRR (2023)
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos
Looped Transformers as Programmable Computers. ICML (2023)
Hadi Daneshmand, Jason D. Lee, Chi Jin
Efficient displacement convex optimization with particle gradient descent. ICML (2023)
Kurtland Chua, Qi Lei, Jason D. Lee
Provable Hierarchy-Based Meta-Reinforcement Learning. AISTATS (2023)
Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen, Jason D. Lee, Yuejie Chi
Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence. SIAM J. Optim. 33 (2) (2023)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML (2023)
Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen
Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. NeurIPS (2023)
Hanlin Zhu, Ruosong Wang, Jason D. Lee
Provably Efficient Reinforcement Learning via Surprise Bound. CoRR (2023)
Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. CoRR (2023)
Zihan Wang, Jason D. Lee, Qi Lei
Reconstructing Training Data from Model Gradient, Provably. CoRR (2022)
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
PAC Reinforcement Learning for Predictive State Representations. CoRR (2022)
Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. AISTATS (2022)
Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. COLT (2022)
Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. CoRR (2022)
Alexandru Damian, Jason D. Lee, Mahdi Soltanolkotabi
Neural Networks can Learn Representations with Gradient Descent. COLT (2022)
Itay Safran, Gal Vardi, Jason D. Lee
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. NeurIPS (2022)
Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR (2022)
Alex Damian, Eshaan Nichani, Jason D. Lee
Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability. CoRR (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR (2022)
Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang
Towards General Function Approximation in Zero-Sum Markov Games. ICLR (2022)
Alex Damian, Jason D. Lee, Mahdi Soltanolkotabi
Neural Networks can Learn Representations with Gradient Descent. CoRR (2022)
Jiaqi Yang, Qi Lei, Jason D. Lee, Simon S. Du
Nearly Minimax Algorithms for Linear Bandits with Shared Representation. CoRR (2022)
DiJia Su, Jason D. Lee, John M. Mulvey, H. Vincent Poor
Competitive Multi-Agent Reinforcement Learning with Self-Supervised Representation. ICASSP (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS (2022)
Itay Safran, Gal Vardi, Jason D. Lee
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias. CoRR (2022)
Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR (2022)
Itay Safran, Jason D. Lee
Optimization-Based Separations for Neural Networks. COLT (2022)
Eshaan Nichani, Yu Bai, Jason D. Lee
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. CoRR (2022)
Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. CoRR (2022)
Wenhao Zhan, Jason D. Lee, Zhuoran Yang
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. CoRR (2022)
Zhiyuan Li, Tianhao Wang, Jason D. Lee, Sanjeev Arora
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent. NeurIPS (2022)
Eshaan Nichani, Yu Bai, Jason D. Lee
Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials. NeurIPS (2022)
Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong
How Important is the Train-Validation Split in Meta-Learning? ICML (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jason D. Lee